AIMC Topic: Electroencephalography

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Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Sleep
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. ...

Outcome Prediction in Postanoxic Coma With Deep Learning.

Critical care medicine
OBJECTIVES: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve ...

Statistical algorithms for emotion classification via functional connectivity.

Journal of integrative neuroscience
Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input to the statistical classifiers. An open-access EEG dataset for emotional state analysis is used to cl...

Percept-related EEG classification using machine learning approach and features of functional brain connectivity.

Chaos (Woodbury, N.Y.)
Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduct...

[A TrAdaBoost-based method for detecting multiple subjects' P300 potentials].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system p...

Automatic Sleep Stages Classification Combining Semantic Representation and Dynamic Expert System.

Studies in health technology and informatics
Interest in sleep has been growing in the last decades, considering its benefits for well-being, but also to diagnose sleep troubles. The gold standard to monitor sleep consists of recording the course of many physiological parameters during a whole ...

Predicting task-general mind-wandering with EEG.

Cognitive, affective & behavioral neuroscience
Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive because it is difficult to track when someone's mind is wandering based only on behavior. The goal of this...

A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitativ...

Comparison of Classifiers for the Transfer Learning of Affective Auditory P300-Based BCIs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The auditory P300-based BCI was improved by changing stimuli. However, the current method needed time for recording training data. The time can be saved by the subject-to-subject transfer learning. However, the suitable classifier for the learning re...

A Convolutional Neural Network for Enhancing the Detection of SSVEP in the Presence of Competing Stimuli.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Stimulus proximity has been shown to have an influence on the classification performance of a steady-state visual evoked potential based brain-computer interface (SSVEP-BCI). Multiple visual stimuli placed close to each other compete for neural repre...